Where \(\mathbb {P}_C\) , \(\mathbb {P}_S\)  are the distributions of content and style training images respectively. A separate decoder is trained for each output scale (64, 128, 256, 512 pixels on the long side). Training converges fairly quickly, and we use models trained for a three epochs on MS-COCO (Wikiart images are sampled independently with replacement for each MS-COCO example). We train using a batch size of 4, and the Adam optimizer [1]} with parameters \(\eta =2e^{-3}, \beta _1=0.0, \beta _2=0.99\) . The same set of decoder models (four total, one for each scale) are used in all experiments.
